How to fix Container exited with code 137 (OOMKilled) in Docker

First, confirm that memory was actually the issue by inspecting the container:

docker inspect -f '{{.State.OOMKilled}}' <container_id>

If this returns true, the OOM killer was responsible. Also check the exit code:

docker inspect -f '{{.State.ExitCode}}' <container_id>

You can also check system logs for OOM events:

# On systemd systems
journalctl -k | grep -i "killed process"

# Or check dmesg
dmesg | grep -i "out of memory"

Before making changes, understand your container's actual memory needs:

# Real-time memory stats for all containers
docker stats

# For a specific container
docker stats <container_name>

Watch the MEM USAGE and LIMIT columns. If usage consistently approaches the limit, you need to increase allocation.

Set an appropriate memory limit using the --memory flag:

# Allocate 1GB of memory
docker run --memory=1g --name myapp myapp:latest

# Combine with memory-swap to prevent swap usage
docker run --memory=1g --memory-swap=1g --name myapp myapp:latest

For Docker Compose, add resource limits to your service:

services:
  myapp:
    image: myapp:latest
    deploy:
      resources:
        limits:
          memory: 1G
        reservations:
          memory: 512M

On macOS and Windows, Docker runs in a VM with limited memory allocation:

Docker Desktop:
1. Open Docker Desktop
2. Go to Settings/Preferences
3. Select Resources → Advanced
4. Increase the Memory slider (e.g., from 2GB to 4GB or more)
5. Click "Apply & Restart"

This is often the quickest fix if you're developing locally and see exit code 137.

If increasing limits only temporarily fixes the issue, your application likely has a memory leak:

For Node.js applications:

# Generate heap snapshots
node --inspect app.js

Use Chrome DevTools to analyze memory over time.

For Java applications:

# Enable JVM memory profiling
docker run --memory=1g \
  -e JAVA_OPTS="-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/dumps" \
  myapp:latest

For Python applications:

# Use memory_profiler
pip install memory_profiler
python -m memory_profiler app.py

Common leak sources: unclosed database connections, circular references, unbounded caches, and accumulating event listeners.

Reduce memory footprint through code optimization:

- Process data in streams/chunks rather than loading entire datasets
- Implement proper connection pooling with max limits
- Use pagination for large database queries
- Clear unused objects and close file handles
- Implement LRU caches with size limits
- Avoid keeping large objects in memory unnecessarily

Example for Node.js streams:

// Bad: loads entire file into memory
const data = fs.readFileSync('large-file.txt');

// Good: processes in chunks
fs.createReadStream('large-file.txt')
  .pipe(processStream)
  .pipe(fs.createWriteStream('output.txt'));